Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

Abstract

Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.

Sequential SAR Coherence Method for the Monitoring of Buildings in Sarpole-Zahab, Iran

“…The information retrieved by SAR in terms of both the intensity (σ 0 ) and coherence (γ) can be used in surface or urban monitoring [1][2][3][4][5][6][7][8][9][10][11][20][21][22][23][24]. Here, the backscattering values were used not for an urban analysis but rather for a simple interpretation of the features in and around Sarpole-Zahab.…”

“…Earthquakes are disastrous natural events that must be constantly monitored to improve the damage response and to prevent numerous deaths. Several spaceborne remote sensing datasets and methods have been proposed to achieve these goals [1][2][3][4][5][6][7][8][9][10][11]. Because earthquakes are significant threats to cities, for successful post-disaster management and rapid damage response, the remote sensing datasets and their derived products (e.g., the extracted footprints of buildings, building heights and corresponding pixel values) should interact with a geographic information system (GIS) that incorporates different characteristics of the city and the remote sensing data.…”

“…Recently, the impacts of coherence changes during the Iran-Iraq earthquake (2017) on the town of Sarpole-Zahab were evaluated using fifteen Sentinel-1 images with temporal baselines less than 100 days and perpendicular baselines less than 150 m [10]. The method used one image as the master image and its coregistration to subsequent slaves.…”

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

“…Change detection (CD) is the process of identifying changes in land cover or land use in the same geographical area over time [1]. CD is one of the most important fields in remote sensing (RS) because it can be used with RS images in many real-world applications, such as the measurement of urban expansion [2], disaster evaluation [3], and crop monitoring [4].…”

Editorial for Special Issue “Advances in SAR: Sensors, Methodologies, and Applications”

“…Behnamian et al [23] report the development of a semi-automated surface water detection technique especially suitable for wetlands. Washaya et al [24] use coherence change detection to identify and monitor damages caused by natural and anthropogenic disasters, including hurricane, forest fire, and earthquake damage detection, in addition to providing an extensive overview of the damages caused by the Syrian War in Aleppo, Raqqa, and Damascus.…”